from tflite_model_maker import image_classifier. For example, a model might be trained with images that contain various pieces of Nov 30, 2022 · How can we configure TFlite in Python to enable the GPU delegate? If it cannot be done currently, what should we change in TFLite to allow Python to use the GPU delegate? It is worth mentioning that we are able to successfully use a GPU with TFlite and C++. For example, you may train a model to recognize photos representing three different types of animals: rabbits, hamsters, and dogs. The following example shows how to use the TensorFlow Lite Python interpreter when provided a TensorFlow Lite FlatBuffer file. So argmax (output [0]) =argmax (-4. Load a model. . If you want to run this tutorial on the microTVM Reference VM, download the Jupyter notebook using the link at the bottom of this page and save it into the TVM directory. 5 or a later version. Jul 27, 2020 · Project Structure. May 23, 2023 · TensorFlow Lite for Microcontrollers is designed to run machine learning models on microcontrollers and other devices with only a few kilobytes of memory. txt having the labels , which if already exists, then overwrites it. py can use either SSD or EfficientNet to process a still image, and TF_Lite_Object_Detection_Yolo. scores: Confidence scores of the detected objects from the TFLite model. from tflite_model_maker import image_classifier from tflite_model_maker. from tflite_model_maker. You could defined Interpreter, allocate_tensors and invoke to get the output from the tflite and compare it with the results from Keras as shown below. tflite tflite_model = TfliteModel. import argparse. prune_low_magnitude = tfmot. load_model(path) call within the scope. . May 26, 2022 · Model Maker allows you to train a TensorFlow Lite model using custom datasets in just a few lines of code. See the persistence of accuracy from TF to TFLite. In this script we are making use of the external adaptor tool of May 31, 2022 · Task could be. Here’s what such a snippet might look like: import numpy as np import tensorflow as tf # Load TFLite model and allocate tensors. tflite visualized_model. tflite and print summary. load_delegate('libedgetpu. The example also demonstrates how to run inference on random input data. Following the instructions here, we built TFlite with GPU support. The input_data means the raw input data, like an image, a text etc. tflite ios/ lib/ Add tflite as a dependency to pubspec. gradle(Module: app) and add the following script below build types which helps in not to compress tflite file when app apk is formed. Here is an example python script showing how to do this. Install with pip: python3 -m pip install tflite-runtime. 0, see this issue. After installing tflite_runtime and making your RPi ready for making predictions, the next step is to make the TFLite model available in the RPi Sep 1, 2022 · classes: Class index of the detected objects from the TFLite model. from transformers import pipeline. So I came across tensorflow lite, which can run on android, but when I looked at a python example for it, I found out that it includes tensorflow- Mar 1, 2024 · The TensorFlow Lite or TFLite export format allows you to optimize your Ultralytics YOLOv8 models for tasks like object detection and image classification in edge device-based applications. 0 The raw API of tflite can be found in this documentation. How do Jul 25, 2019 · Create a tflite interpreter and (optionally) perform inference. Interpreter) in the Python terminal to get detailed documentation on the Running inference using TensorFlow Lite . python detect. from_saved_model(saved_model_dir) # path to the SavedModel directory. You can use pre-trained models with TensorFlow Lite, modify existing models, or build your own TensorFlow models and then convert them to TensorFlow Lite format. On the hardware board, set the environment variable TFLITE_MODEL_PATH to the location of the TFLite model. core import TfliteModel # Load the . Train a custom object detection model. load("en_core_web_sm") nlp. image_classifier import DataLoader. It directly binds to TFLite C API making it efficient (low-latency). Then run the script provided by the package: May 29, 2018 · As indicated in the answer Note: the converted tflite model is taking logits as the output instead of softmax for efficiency. keras. OperatorCode. TFLiteConverter. Jun 25, 2019 · When I load this tflite file and try to make predictions on the same input images, it always predicts 'ZERO' which is the first class and with probability = 0. I am executing a TFLite model on python in order to make predictions based on input data. count: Number of detected objects from the TFLite model. # The function `get_tensor()` returns a copy of the tensor data. Later you can load this file to deserialize your model and use it to make new predictions. Jan 28, 2020 · I have an SSD tflite detection model that I am running with Python on a desktop computer. import tensorflow_model_optimization as tfmot. In the mobile app, you can simply take the argmax of the logits to get the class. # Run the model with TensorFlow to get expected results. Import with tflite_runtime as follows: import tflite_runtime. 00 I get the same results when loading my tflite model in the Android Image classification example app from Tensorflow repo's. May 10, 2022 · 1. pt, yolov5m. Mar 4, 2021 · android/ assets/ model. 003922. When deploying a TensorFlow neural-network model for on-device ML applications, it streamlines the process of adapting and converting the model to specific input data. Test Platform The codes are tested successfully in below platform. Dec 13, 2021 · Also note that TFLite models are executed using WASM backend, no other option (mostly due to original philosophy of tflite which is CPU execution of int quantized models for consumption on the edge where GPU or FPU are not that prevalent) For instance, it could have an end-to-end image classification example that utilizes this library with just 4 lines of code, each of which representing one step of the overall process. 5 . In that blog post, they have provided codes to run it on Android and IOS Nov 17, 2021 · edited. Objective-C API reference (coming soon) C++ API reference. model. However, they have only provided one MobileNet v1 SSD model with Tensorflow lite which is described here. Jun 4, 2023 · 1. open( input_path ) img. recommendation module: APIs to train an on-device recommendation model. pt is the 'small' model, the second-smallest model available. Photo by Elijah Hiett on Unsplash. This created a static library called libtensorflow-lite. Overview; ResizeMethod; adjust_brightness; adjust_contrast; adjust_gamma; adjust_hue Aug 18, 2022 · Learn more. yolov5s6. pt. input_details = interpreter. This tutorial is an introduction to working with microTVM and a TFLite model with Relay. See compression benefits of PQAT model. data = DataLoader. 907143 ) = 1. An image classification model is trained to recognize various classes of images. tflite") May 15, 2023 · The API reference documentation provides detailed information for each of the classes and methods in the TensorFlow Lite library. For example, here are the steps to train an image classification model. Run help(tf. py. Raw. Then: cd to the TVM directory. load_flatbuffer_file(tflite_path) # Generate a summary of the . Interpreter(model_path="MODELNAME. int32) interpreter. import os. I am trying to get the model (which ran successfully before it was converted to. object_detector module: APIs to train an object detection model. 2. Sep 10, 2021 · The problem is in the line hand = model_hands. interpreter = tf. An object detection model is trained to detect the presence and location of multiple classes of objects. Fork 1 1. 0. Jul 19, 2023 · Now that we have the model and our development environment ready, the next step is to create a Python snippet that allows us to load this model and run inferencing with it. TensorFlow sometimes leaves compability hanlding of the TFLite model to the users. Aug 26, 2022 · model_spec module: APIs for the model spec of TFLite Model Maker. save and tf. The automatic codegen strategy is designed To load a TensorFlow Lite (TFLite) model in a Python script, you can use the TensorFlow Lite Interpreter provided by the TensorFlow library. This data format is also required by Next, take the custom TFLite model that was trained and downloaded from the Colab notebook and move it into the C:\tflite1 directory. tflite_model can be saved to a file and loaded later, or directly into the Interpreter. The MobileNet test can serve as a usage example of parsing models. invoke() TfLite Delegate Quick Start Guide. txt and model. 8. Offers acceleration support using NNAPI, GPU delegates on Android, Metal and CoreML Interpreter interface for running TensorFlow Lite models. (2) There should be four output tensors for locations, classes, scores, and number of detection. lite provides Interpreter. Here's how you can load and use a TFLite model in a Python script: Install TensorFlow: Make sure you have TensorFlow installed Nov 16, 2023 · Integer quantization is an optimization strategy that converts 32-bit floating-point numbers (such as weights and activation outputs) to the nearest 8-bit fixed-point numbers. The TensorFlow Lite Interpreter allows you to run inference on TFLite models. yaml; dependencies: flutter: sdk: flutter tflite: ^1. # Use `tensor()` in order to get a pointer to the tensor. The following example shows how to run the training procedure using the runSignature method: Jan 23, 2021 · Flutter requires two files: labels. Jun 7, 2016 · Pickle is the standard way of serializing objects in Python. evaluate_tflite('model. That being said, your TFLite interpreter can run CPU models too (all we did was add the experimental delegate to handle that edgetpu-custom-op). TensorFlow Lite (abbr. Run Inference in your dart script. Finally, I quantize the TFLite model to int8. Deploy your model. You can use the pickle operation to serialize your machine learning algorithms and save the serialized format to a file. js model (Python environment) Importing a TensorFlow model into TensorFlow. Note that I compiled this natively on my target system. visualize model. Navigate to the location where you downloaded the Android project that corresponds to your Copy TFLite Model to the Raspberry Pi board. Jul 1, 2022 · Used in the tutorials. Python API reference. The above requirements actually reflect the object detection tasks. Jun 4, 2020 · 1) Created a python script where I am using MobileNetV2 model (pre-trained on ImageNet for 1000 classes) of Keras (backend Tensorflow) and tested it with images to see if it is returning the correct labels after detecting objects correctly. class OpsSet: Enum class defining the sets of ops available to generate TFLite models. 7. Choose your preferred platform from the list below. Define the model and apply the sparsity API. converter = tf. Tensorflow has recently released its object detection API for Tensorflow 2 which has a very large model zoo. tflite_interpreter. A tf. Dec 5, 2020 · Step 1: Convert Tensorflow’s model to TF. array(img, dtype=np. tflite file and choose Download to download it to your local computer. The core runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic models. I've looked at the examples on Google's websites and other posts on Stack Overflow and there seems to be two ways I can tackle this: using SavedModel type . a. TensorFlow Lite provides optimized pre-trained models Sep 10, 2018 · The saved model can be loaded to python script or to other platforms and languages. The rest of the classes are always 0. Aug 30, 2023 · Netron is the easiest way to visualize a TensorFlow Lite model. In this guide, we'll walk through the steps for converting your models to the TFLite format, making it easier for your models to perform well on various This is mainly a demostration of how to get the possible things as well as their location from the model. searcher module: APIs to create the Jun 8, 2018 · this worked for me, thank you. TF Lite) is an open-source, cross-platform framework that provides on-device machine learning by enabling the models to run on mobile, embedded, and IoT devices. If you have downloaded the Arm NN Github binaries or built the TfLite delegate yourself, then this tutorial will show you how you can integrate it into TfLite to run models using python. Since TensorFlow Lite pre-plans tensor allocations to optimize inference, the user needs to call allocate_tensors() before any inference. question_answer module: APIs to train a model that can answer questions based on a predefined text. js web format. Interpreter(model_content=tflite_model) Apr 4, 2021 · Basically, the object detector API requires the following requirements: (1) There should be only one input tensor for representing an uncompressed image. TensorFlow Lite Task Library: deploying object detection models on mobile in a few lines of code. The API is similar to the TFLite Java and Swift APIs. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. The generated python package is not friendly Oct 29, 2022 · I'm new to tensorflow and object detetion, and any help would be greatly appreciated! I got a database of 50 photos, used this video to get me started, and it DID work with Google's Sample Model (I'm Aug 30, 2023 · The task of identifying what an image represents is called image classification. keras API and then convert the model to a TFLite model. tflite_model = converter. This section describes the signature for Single-Shot Detector models converted to TensorFlow Lite from the TensorFlow Object Detection API. models. This results in a smaller model and increased inferencing speed, which is valuable for low-power devices such as microcontrollers. Jan 22, 2024 · Step 1. 627849, 1. h. See Deploy and manage custom models. Download, Run Model. I can save and load the "normal" tensorflow model with the API model. Sample ML apps for Android, iOS and Raspberry Pi. Now go to build. @karndeepsingh we've made inference with TFLite easy: python export. predict(X)[0]. # Load input data specific to an on-device ML app. Import the required modules. Install TensorFlow 2. py is the YOLO version. Take a look at the following tutorial for more information: https The officially supported TensorFlow Lite Micro library for Arduino resides in the tflite-micro-arduino-examples GitHub repository. tflite") interpreter. 1')]) The file passed to load_delegate() is the Edge TPU runtime library, and you should have Nov 3, 2020 · In short, change from_keras_model => from_keras_model_file. For detail:. pb files or using the tfliteconverter. pt and yolov5x. The output of Sep 28, 2021 · 1. Jun 16, 2021 · # Step 5: Evaluate the TensorFlow Lite model model. If you use tensorflow v2 the converter from_keras_model is found in tf. For example MinMaxScaler (subtract minimum from a value and divide by the difference between the minimum and maximum). Builtin opcode helper: The opcode is encoded as digits which is hard to parse for human. Get started with TensorFlow Lite. tflite summary = tflite_model. Drag the autocomplete. Aug 21, 2020 · Aug 21, 2020. All you need is a TensorFlow model converted to TensorFlow Lite. Interpreter(model_path="model. In Java, use the Interpreter class to load a model and drive model training tasks. output tensor is send back to output and logged to console. mediapipe. Customize the TensorFlow model. etc. get_input_details() Jan 13, 2024 · import pytextrank. An example with opencv/tflite object detection combo. This example loads . py --weights yolov5s. For more detail, you could refer to Colab for image classification. Jul 7, 2020 · Hi, think of scaling as a mathematical operation to bring the values into the range [0,1]. Use the tensorflowjs package for conversion. Interpreter(model_content=tflite Feb 22, 2021 · When you compile a Coral model, it maps all the operations it can to a single TPU Custom OP - for example: . utils. pt --include tflite. The example below demonstrates how you can train a logistic regression Nov 12, 2021 · Classes. Each . If Netron cannot open your TensorFlow Lite model, you can try the visualize. 0 alpha on Colab. pb file or a May 15, 2023 · Step 3. Otherwise, you can run this script with Bazel. See full list on tensorflow. Mar 29, 2024 · Last active 2 months ago. wav files. You are trying to call function predict on a string you defined above as model_hands = 'converted_model. The problem is that I cannot include tensorflow and keras in my code because kivy doesn't allow apk conversion with it. The ‘w’ in the code creates a new file called labels. To run the CPU version, simply pass Jul 1, 2019 · TensorFlow Lite comes with a script for the compilation on machines with the aarch64 architecture. tflite'. It doesn't require operating system support, any standard C or C++ libraries, or dynamic memory allocation. from_folder('flower_photos/') Step 3. txt is a text file containing the classes: Jun 14, 2020 · 1. Hi @glenn-jocher , I want to do some additional stuff with object detection. Mar 9, 2024 · In this example, you start the model with 50% sparsity (50% zeros in weights) and end with 80% sparsity. e. TensorFlow Lite models can perform almost any task a The generated python package is not friendly to use sometimes. js SDKs. Dec 31, 2021 · I'm trying to make an ML app with kivy, which detects certain objects. BuiltinCode(): maintains API compability in 2. by the way the tensorflow lite model doesn't give the same results as the python model, the differences are very big for example python accuracy 79% and tflite accuracy 50% do you know how to improve this? maybe a paramater or a better export function, I am currently using toco convert in the frozen graph May 7, 2024 · Convert a SavedModel (recommended) The following example shows how to convert a SavedModel into a TensorFlow Lite model. There are three major use cases that TFLite Support targets at: Provide ready-to-use APIs for users to interact with the model. zip. js is a two-step process. tflite model file downloaded from the last step into the app/src/main/assets/ folder in Android Studio. Args. tools. For more information on setting environment variables, see Prerequisites for Deep Learning with TensorFlow Lite Models. pt, along with their P6 counterparts i. (If you don't have a model converted yet, you can experiment using the model provided with the example linked below. import tensorflow as tf. To install the in-development version of this library, you can use the latest version directly from the GitHub repository. TensorFlow Lite uses TensorFlow models converted into a smaller, more efficient machine learning (ML) model format. To compile tflite model for Google Coral Edge TPU I need quantized input and output as well. prune_low_magnitude. import cv2. We have introduced several enhancements: Easy import: A single import tflite ( example) to replace importing every classes and funtions in tflite ( example ). class Interpreter: Interpreter interface for running TensorFlow Lite models. py --weights yolov5s-fp16. First, download the compiled TensorFlow Lite model file using the left sidebar of Colab. g. load() input_data = np. Tap on the button and capture some pictures of objects around you. E. load_model. send a float array to the graph (say of length = 1 and all values = 0 for simplicity) tflite model adds 1 to each element of the input tensor. tflite) to classify test images. 대부분의 경우, API 설계 시 사용 편의성보다는 성능에 치중합니다. Jul 10, 2020 · Compile and Train the model; Keras models to TFLITE format; 1. # Convert the model. wav file corresponds to an example. We then check the model to ensure its schema version is compatible with the version we are using: Aug 30, 2023 · Model description. img = Image. tflite', test_data) Check out this notebook to learn more. import numpy as np. Right-click on the model_edgetpu. If you downloaded it from Colab, it should be in a file called custom_model_lite. The following example from here shows how to load tflite model from local file using python scripts. After you add a custom model to your Firebase project, you can reference the model in your apps using the name you specified. Step 2. Interpreter(model_content=tflite_model) interpreter. so. TensorFlow Lite – The Tflite Model. 0 License , and code samples are licensed under the Apache 2. 4. As these are API breaking change that can be easily fixed, we do this in the tflite package. In the previous step, you integrated a pre-trained TFLite object detection model to the Android app and saw for yourself that it can detect common objects, such as bowls or dining tables, in sample images. # Run the model with TensorFlow Lite. Mar 9, 2024 · Prune and fine-tune the model to 50% sparsity. runs/exp/weights/best. (If you haven't trained a model yet and just want to test one out, download my "bird, squirrel, raccoon" model by clicking Now that you have converted the GPT-2 model into TensorFlow Lite, you can finally deploy it in the app. tflite. Interpreter to load the model and run an inference as follows Jan 23, 2023 · flutter run. add_pipe("textrank") When I run it : example_text= """Deep learning. tflite the variable values were: top_K = [458 653 835 514 328] i = 226 As you can see the values are very different which i assume is because they are different models but i am not sure how to translate that to human readable output. 1. In the following commands, replace targetDir with the destination folder . This page has the instructions on how to load a TFLite model with python: # Load the TFLite model and allocate tensors. dataset. Swift API reference. Click Run in the navigation menu and then wait for the app to load. If you need more features from TFLite, then you need to install the complete TensorFlow package. Feb 14, 2022 · Based on @Karim Nosseir's answer, I used the signature method to access the inputs and outputs of my model. Apr 3, 2024 · The argument must be a dictionary mapping the string class name to the Python class. Step 1. pip install tensorflowjs. Jun 18, 2018 · Using the interpreter from a model file. pt or you own custom training checkpoint i. TensorFlow Lite Model Maker. 7498368, -5. yolov5s. This requires you clone the repo into the folder that holds libraries for the Arduino IDE. TEST_CASES = 10. - tensorflow/tflite-micro Nov 12, 2023 · This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. For example, your TensorFlow Lite code will ordinarily have a line like this: interpreter = tflite. Google Colaboratory makes it really easy to setup Python notebooks in the cloud. tflite model file and prints a summary: # Import the TfliteModel class from mltk. custom_object_scope with the object included in the custom_objects dictionary argument, and place a tf. Open Android Studio and select “Open Project” from the welcome screen or the “File” menu. tflite models are not included, you will need to download the models before you run the code. Run the app. May 3, 2022 · Inference Time. summary() # Print the summary to the Jul 2, 2019 · The resulting model will be fully quantized but still take float input and output for convenience. load_model(path, custom_objects={'CustomLayer': CustomLayer}) Use a tf. image_width: Width of the input image. html. With the model (s) compiled, they can now be run on EdgeTPU (s) for object detection. org Jun 25, 2021 · In my pipeline, I train my model with the tf. ) May 7, 2024 · On Android, you can perform on-device training with TensorFlow Lite using either Java or C++ APIs. convert() Jun 9, 2023 · 5. # Test the model on random input data. 3. In the following code, the model is instantiated using data from a char array, g_model, which is declared in model. lite. class Optimize: Enum defining the optimizations to apply when generating a tflite model. Aug 23, 2023 · Install the TensorFlow Lite interpreter with Python using the simplified Python package, tflite-runtime. from_keras_model, but it is for loaded model instead of a path as you have shown. Deploy your custom TensorFlow models using either the Firebase console or the Firebase Admin Python and Node. This means that this model will only work on the TPU. As for now, my script below takes a single image as an input for inference and it works fine: # Load TFLite model and allocate tensors. Other options are yolov5n. Explore the guide. pt, yolov5l. py script in our repository. If you have a model built in python then you can find out the signature like in the answer and use it as shown below: Python signature: {'serving_default': {'inputs': ['dense_6_input'], 'outputs': ['dense_8']}} Android java use: Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors). #Define typical spacy pipeline. interpreter as tflite Getting a trained model. With free Just the Interpreter class is available, which makes predictions based on an existing TFLite model. contrib. tflite and . set_tensor(input_details[0]['index'], input_data) interpreter. image_height: Height of the input image. The name for each folder is the name of the audio class. If you're using TF 2. Dec 7, 2023 · TensorFlow Lite Flutter plugin provides a flexible and fast solution for accessing TensorFlow Lite interpreter and performing inference. I followed the guide here to do this, even though I had to modify the Makefile slightly. wav file is mono (single-channel) and has the typical 16 bit pulse-code modulation (PCM) encoding. class RepresentativeDataset: Representative dataset used to optimize the model. data. 0 License . Example 1: Load . , while the target means some ground truth of the raw TensorFlow 추론 API는 Android, iOS 및 Linux 와 같은 가장 일반적인 모바일/임베디드 플랫폼에서 사용할 수 있게 여러 프로그래밍 언어로 제공됩니다. In the model, I see that the first network layer converts float input to input_uint8 and the last layer converts output_uint8 to the float output. This is achieved by the TFLite Support Codegen tool, where users can get the model interface (contains ready-to-use APIs) simply by passing the model to the codegen tool. The next step is to get a trained model that would run on the device. Star 7 7. The problem is, I want to deploy this trained model to Firebase mlkit as a tflite file. Image classification with TensorFlow Lite Model Maker. Load input data specific to an on-device ML app. [ ] # Load TFLite model and allocate tensors. python -m tensorflow. # Get input and output tensors. , tf. pip install tensorflow == 2. get_tensor_details() will give a list of dictionaries that have weights, biases, their scales, zero_points. sparsity. C API reference. May 27, 2022 · This page shows how you can start running TensorFlow Lite models with Python in just a few minutes. The model has been trained on AutoML-Google-API, then I downloaded its TFLite model. Returns: A list of Detection objects detected by the TFLite model. TensorFlow Lite Task Library is a cross-platform library which simplifies TensorFlow Lite model deployments on Jul 1, 2022 · The root data_path folder contains a number of folders. Enhancements. Within each folder, there are a number of . Interpreter(model_path) So change it to this: interpreter = tflite. See end-to-end examples with complete instructions to train, test and deploy models on mobile devices. Android (Java) API reference. For example, the following code snippet is an example script on how to run Inference on an Image where labels. Fine-tune the model and evaluate the accuracy against baseline. In the comprehensive guide, you can see how to prune some layers for model accuracy improvements. 0 pip install tflite == 2. Interpreter(model_path, experimental_delegates=[tflite. So, this loading of model and extra stuff with object detection has to done in a pipeline. lite model on Python, for model trouble-shooting before deployment to mobile platform. TF_Lite_Object_Detection. allocate_tensors() # Get input and output tensors. tflite_cv_objdetect. Nov 12, 2022 · It's recommended to install the version that same as the TensorFlow that generates the TFLite model. I believe what you want to do is load the model using an Interpreter, set the input tensor, and invoke it. So second class is the output. I used tf. First, convert an existing model to the TensorFlow. Having such a code would surely be a great step ahead while experimenting with the framework. TensorFlow Lite는 소형 기기에서 빠른 추론을 Dec 18, 2020 · you can implement and test it like: # Load TFLite model and allocate tensors. Apply QAT and PQAT and check effect on model sparsity in both cases. nlp = spacy. Dataset object that contains a potentially large set of elements, where each element is a pair of (input_data, target). allocate Pre-trained models and datasets built by Google and the community The . Deep learning is a class of machine learning algorithms that[9]: 199–200 uses multiple layers to progressively extract higher-level features from the Aug 26, 2022 · : Loads data and retrains the model based on data for image classification. There are two ways to generate TensorFlow Lite models: Initialize the TFLite interpreter to try it out. Overview The programme creates a TFlite interpreter in the Python environment which supports inteferences to be run to test the accuracy of the converted TFlite model either from a frozen . Mar 20, 2019 · 2. Jan 27, 2019 · During the execution with tflite model: mobilenet_quant_v1_224. tflite. Two APIs added to make it easy to use. The TensorFlow Lite Model Maker Library enables us to train a pre-trained or a custom TensorFlow Lite model on a custom dataset. To use saved tflite model, tensorlfow. op ok pj wv qr li yx hj ff ak